Toxicity analysis and prediction are of paramount importance to human health and environmental protection. Existing computational methods are built from a wide variety of descriptors and regressors, which makes their performance analysis difficult. For example, deep neural network (DNN), a successful approach in many occasions, acts like a black box and offers little conceptual elegance or phys...

Mass segmentation is an important task in mammogram analysis, providing effective morphological features and regions of interest (ROI) for mass detection and classification. Inspired by the success of using deep convolutional features for natural image analysis and conditional random fields (CRF) for structural learning, we propose an end-to-end network for mammographic mass segmentation. The n...

We present a multi-modal Deep Neural Network (DNN) approach for bird song identification. The presented approach takes both audio samples and metadata as input. The audio is fed into a Convolutional Neural Network (CNN) using four convolutional layers. The additionally provided metadata is processed using fully connected layers. The flattened convolutional layers and the fully connected layer o...

Compressive sensing (CS), aiming to reconstruct an image/signal from a small set of random measurements has attracted considerable attentions in recent years. Due to the high dimensionality of images, previous CS methods mainly work on image blocks to avoid the huge requirements of memory and computation, i.e., image blocks are measured with Gaussian random matrices, and the whole images are re...

Eye health has become a global concern and attracted broad attention. Over the years, researchers have proposed many state-of-the-art convolutional neural networks (CNNs) to assist ophthalmologists in diagnosing ocular diseases efficiently precisely. However, most existing methods were dedicated constructing sophisticated CNNs, inevitably ignoring trade-off between performance model complexity....

Polarimetric synthetic aperture radar (PolSAR) images contain useful information, which can lead to extensive land cover interpretation and a variety of output products. In contrast optical imagery, there are several challenges in extracting beneficial features from PolSAR data. Deep learning (DL) methods provide solutions address feature extraction challenges. The convolutional neural networks...

Recent studies have shown that aggregating convolutional features of a pre-trained Convolutional Neural Network (CNN) can obtain impressive performance for a variety of visual tasks. The symmetric Positive Definite (SPD) matrix becomes a powerful tool due to its remarkable ability to learn an appropriate statistic representation to characterize the underlying structure of visual features. In th...

In this article, the task of classifying handwritten digits using a class of multilayer feedforward network called Convolutional Network is considered. A convolutional network has the advantage of extracting and using features information, improving the recognition of 2D shapes with a high degree of invariance to translation, scaling and other distortions. In this work, a novel type of convolut...

In this paper, we propose a parallel Convolutional Neural Network architecture for the task of classifying acoustic scenes and urban sound scapes. A popular choice for input to a Convolutional Neural Network in audio classification problems are Mel-transformed spectrograms. We, however, show in this paper that a ConstantQ-transformed input improves results. Furthermore, we evaluated critical pa...

The explosive computation and memory requirements of convolutional neural networks (CNNs) hinder their deployment in resource-constrained devices. Because conventional CNNs perform identical parallelized computations even on redundant pixels, the saliency various features an image should be reflected for higher energy efficiency market penetration. This paper proposes a novel channel spatial ga...